Prediction Engine for Data Object Generation Forecasting
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Solution Overview
Problem
Existing prediction systems in cloud computing environments fail to accurately forecast the generation of data objects, as they do not account for industry-specific factors and complex relationships between events, leading to inaccurate predictions.
Innovation Solution
A prediction engine is implemented in an on-demand database service environment that uses historical data from filtered sources based on criteria like industry, region, season, and time to predict future data object generation, incorporating additional factors such as new products, campaigns, and social data, and applying weights to these factors for more accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing prediction systems are used in cloud computing environments, then predictions can be generated, but the accuracy of predictions is insufficient due to failure to account for industry-specific factors and complex relationships between events
Solution Approach 1:
The prediction system segments the analysis by dividing data sources into multiple categories including industry-specific data, regional data, seasonal data, and event-specific data. Each segment is analyzed separately with appropriate weighting, allowing the system to capture nuanced industry-specific factors while maintaining overall prediction accuracy.
Solution Approach 2:
The system dynamically adjusts prediction parameters by incorporating multiple data sources with different weights. The weighting parameters are configured based on relevance to specific prediction targets, allowing the system to adapt to different industry contexts and event types while maintaining high prediction accuracy.
2Measurement precision
If multiple data sources and factors are incorporated into the prediction system, then prediction accuracy improves, but the complexity of the system increases
Solution Approach 1:
The system introduces a prediction engine as an intermediary component that manages multiple data sources and applies weighted algorithms. This intermediary layer simplifies the complexity by providing a unified interface for data integration and prediction generation, hiding the underlying complexity from users while maintaining high accuracy through multiple factors.
Solution Approach 2:
The prediction engine is designed as a universal system that can handle multiple types of data sources (industry data, regional data, seasonal data, event data) and apply appropriate weighting algorithms across different prediction scenarios. This multi-functional design reduces overall system complexity by using a single versatile platform rather than separate specialized systems.
3Measurement precision
If historical data from multiple sources is analyzed to improve predictions, then forecasting accuracy improves, but the time and resources required for data processing increase
Solution Approach 1:
The system performs preliminary actions by pre-configuring data source weights and filtering criteria based on historical performance and relevance. This preliminary setup reduces processing time during actual prediction operations, as the system can quickly apply predetermined weights to multiple data sources rather than determining optimal weights in real-time, thereby maintaining high accuracy while reducing processing time.
Data Source
AI summary
Disclosed are some implementations of systems, apparatus, methods, and computer program products for facilitating the prediction of the quantity and/or qualities of new data objects of a particular data object type to be generated based upon past generation of data objects of the particular data object type. Data that is used to generate predictions is obtained and filtered according to criteria that are configurable. In some implementations, the criteria indicate an industry for which predictions are generated, a geographic region for which predictions are generated, and/or time period criteria indicating a time period for which the predictions are generated. Predictions may be generated using a computer-generated model, which may be associated with the particular data object type.